


'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_lowerCAmelCase     :							List[Any]    =				{
    """configuration_resnet""": ["""RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ResNetConfig""", """ResNetOnnxConfig"""]
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowerCAmelCase     :							str    =				[
		    """RESNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """ResNetForImageClassification""",
		    """ResNetModel""",
		    """ResNetPreTrainedModel""",
		    """ResNetBackbone""",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowerCAmelCase     :							Union[str, Any]    =				[
		    """TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """TFResNetForImageClassification""",
		    """TFResNetModel""",
		    """TFResNetPreTrainedModel""",
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowerCAmelCase     :							Dict    =				[
		    """FlaxResNetForImageClassification""",
		    """FlaxResNetModel""",
		    """FlaxResNetPreTrainedModel""",
		]

if TYPE_CHECKING:
		from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_resnet import (
				    RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ResNetBackbone,
				    ResNetForImageClassification,
				    ResNetModel,
				    ResNetPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_resnet import (
				    TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFResNetForImageClassification,
				    TFResNetModel,
				    TFResNetPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
		import sys

		_lowerCAmelCase     :							Any    =				_LazyModule(__name__, globals()["__file__"], _import_structure)











"""simple docstring"""




import random



def        a__					(							snake_case__			,					snake_case__			,					snake_case__ = False							)		->   dict:
			lowerCamelCase      =   {i: [] for i in range(snake_case__							)}

			# if probability is greater or equal than 1, then generate a complete graph
			if probability >= 1:
						return complete_graph(snake_case__							)
			# if probability is lower or equal than 0, then return a graph without edges
			if probability <= 0:
						return graph

			# for each couple of nodes, add an edge from u to v
			# if the number randomly generated is greater than probability probability
			for i in range(snake_case__							):
						for j in range(i + 1			,					snake_case__							):
									if random.random() < probability:
												graph[i].append(snake_case__							)
												if not directed:
															# if the graph is undirected, add an edge in from j to i, either
															graph[j].append(snake_case__							)
			return graph



def        a__					(							snake_case__							)		->   dict:
			return {
			    i: [j for j in range(snake_case__							) if i != j] for i in range(snake_case__							)
			}


if __name__ == "__main__":
					import doctest

					doctest.testmod()



